Method and apparatus for opportunistic user scheduling of two-cell multiple user mimo

ABSTRACT

An apparatus and a method for opportunistic user scheduling of two-cell multiple user Multiple Input Multiple Output (MIMO) by a base station, the method comprising: broadcasting signals through random beams to users; and receiving Channel Quality Information (CQI) from best K user set. The CQI is calculated based on all possible combinations of transmit beamforming vectors.

PRIORITY

This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Application No. 61/532,610, which was filed in the U.S. Patent and Trademark Office on Sep. 9, 2011, the entire content of which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to two-cell multiple user Multiple Input Multiple Output (MIMO), and more particularly, to a method and an apparatus for opportunistic user scheduling of two-cell multiple user MIMO.

BACKGROUND ART

Over the past few years, a significant amount of research has gone into making various techniques for enhancing spectrum reusability reality. The spatial diversity techniques such as MIMO wireless systems have been widely studied to improve the spectrum reusability. Recently, the scope of the spatial diversity is extended to MIMO network wireless systems including the interference network, relay network, and multicellular network. Network MIMO systems are now an emphasis of IMT-Advanced and beyond systems. In these networks, out-of-cell (or cross cell) interference is a major drawback. Network MIMO systems, when properly designed, could allow coordination between nodes that would increase the quality of service in an interference limited area. Before network MIMO can be deployed and used to its full potential, there are a large number of challenging issues. Many of these deal with joint processing between nodes (e.g., see the references in D. Gesbert, S. Hanly, H. Huang, S. Shamai, O. Simeone, and W. Yu, “Multi-cell MIMO cooperative networks: a new look at interference,” IEEE Jour Select. Areas in Commun., vol. 28, no. 9, pp. 1380-1408, December 2010).

Interference alignment is transmitters/receivers joint processing that generates an overlap of signal spaces occupied by undesired interference at each receiver while keeping the desired signal spaces distinct (e.g., see the references in V. Cadambe and S. Jafar, “Interference alignment and degrees of freedom of the k-user interference channel,” IEEE Trans. Info. Theory, vol 54, no. 8, pp. 3425-3441, August 2008. and C. Suh and D. Tse, “Interference Alignment for Cellular Networks,” Proc. Of Allerton Conference on Communication, Control, and Computing, September 2008. and V. Cadambe and S. Jafar, “Interference alignment and the degrees of freedom of wirelessX networks,” IEEE Trans. Info. Theory, vol 55, no. 9, pp. 3893-3908, September 2009). However, the joint processing between transmitters and receivers for interference alignment requires full channel knowledge at all nodes, which is arguably unrealistic. Recent work on the limited feedback explores the scales of the required feedback bits with respect to the required throughput or SINR (e.g., see the references Thukral and H. Bolcskei, “Interference alignment with limited feedback,” in Proc. IEEE Inrl. Symposium on Info. Theory, June, 2009. and B. Khoshnevis, W. Yu, and R. Adve, “Grassmannian beamforming for MIMO amplify-and-forward relaying,” IEEE Journals on Sel. Areas in Commun., vol. 26, pp. 1397-1407, August 2008). However, the amount of CSI feedback in (e.g., see the references in Thukral and H. Bolcskei, “Interference alignment with limited feedback,” in Proc. IEEE Inrl. Symposium on Info. Theory, June, 2009. and B. Khoshnevis, W. Yu, and R. Adve, “Grassmannian beamforming for MIMO amplify-and-forward relaying,” IEEE Journals on Sel. Areas in Commun., vol. 26, pp. 1397-1407, August 2008.) to ensure the required performance is excessive and only gives marginal performance improvement per additional feedback. In addition, the feedback scheme in (e.g., see the references in Thukral and H. Bolcskei, “Interference alignment with limited feedback,” in Proc. IEEE Inrl. Symposium on Info. Theory, June, 2009. and B. Khoshnevis, W. Yu, and R. Adve, “Grassmannian beamforming for MIMO amplify-and-forward relaying,” IEEE Journals on Sel. Areas in Commun., vol. 26, pp. 1397-1407, August 2008.) assumes huge information sharing between backhauls of the transmitter.

SUMMARY OF THE INVENTION

Accordingly, the present invention is designed to address at least the problems and/or disadvantages described above and to provide at least the advantages described below.

Accordingly, an aspect of the present invention is to provide a method for opportunistic user scheduling of two-cell multiple user MIMO.

In accordance with an aspect of the present invention, a method is provided for opportunistic user scheduling of two-cell multiple user MIMO by a base station, the method comprising: broadcasting signals through random beams to users; and receiving Channel Quality Information (CQI) from best K user set. The CQI is calculated based on all possible combinations of transmit beamforming vectors.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certain embodiments of the present invention will be more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which:

FIG. 1. illustrates system model choosing the best K user among j users on two-cell multiple user broadcasting channel;

FIG. 2 illustrates system model when the best K users Π=(π₁, . . . , π_(x)) are scheduled in each of cells;

FIG. 3 illustrates sumrates per cell for MIUS schemes;

FIG. 4 illustrates sumrate performance for MSUS schemes; and

FIG. 5 illustrates sumrate performance for MSUS employing 6 bits and 8 bits of matrix codebook.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Various embodiments of the present invention will now be described in detail with reference to the accompanying drawings. In the following description, specific details such as detailed configuration and components are merely provided to assist the overall understanding of these embodiments of the present invention. Therefore, it should be apparent to those skilled in the art that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

In this work, we investigate the opportunistic user scheduling in interfering multiuser MIMO network where J users are associated with each transmitter and the selected K users together with their transmitters construct a two-cell multiuser MIMO broadcast channel. Other than feeding back excessive amount of CSI to transmitter, we consider a scenario where each transmitter broadcasts random beams (known at both sides of the transmitter and receiver) to users and the user sends back its CQI to transmitter for the opportunistic user selection. Backhaul between transmitters to allow information sharing is not assumed. We present exhaustive user scheduling approach and opportunistic user alignment scheme based on the interference alignment approach in (e.g., see the references in T. Kim, D. Love, and B. Clerckx, “Spatial Degrees of Freedom of the Multicell MIMO MAC,” submitted in IEEE Global Communications, April, 2011). For the exhaustive user scheduling, random beams are the actual transmit beams, but in the opportunistic user alignment the random beams are not the actual transmit beams. It is rather used for selecting users and once the users are selected transmit beams are designed at the transmitter. For each case, we define efficient CQI measure to be fed back to transmitter for user scheduling and we also address the optimal design of the post processing matrix to minimize the interference (inter user interference+out-of-cell interference or out-of-cell interference) or to maximize the sumrate.

1. System Model

FIG. 1. Illustrates system model choosing the best K user among j users on two-cell multiple user broadcasting channel.

Referring to FIG. 1, the base station has M antennas and each user is equipped with N antennas. There are total J users in the cell and only K users (K≦J) are selected and served by each BS. We use an index mj to denote the jth user in the cell m, where jεI and I=(1, . . . , J).

We use an index mπ_(k) to indicate kth scheduled user in cell m where 5εΠ=(π₁, . . . , π_(K))⊂I.

We assume that each user has N=Kβ antennas and each base station is equipped with M=Kβ+β antennas. The base station sends Kβ streams to provide 3 streams to each of K users in the cell. The transmitter has no access for the perfect channel state information (CSI) at users, rather it has partial CSI for scheduling users. CSI sharing among base stations through backhaul is not allowed and only partial CSI from serving users is available at the base station.

In the first step, the base station m broadcasts signals through K random beams (V_(mi))_(ieK), K=(1, . . . , K), to users where V_(mi)εC^(Kβ+β))

^(and V) _(mi)*V_(mi)=I_(β). Then, the received signal at jth user in the cell m can be determined as shown in Equation (1).

Y _(mj) =H _(mj,m) s _(m) +H _(mj, m) s _(m) +z _(mi)  (1)

In Equation (1), m is defined as m=(1,2)\m. The H_(mj,m)εC^(N×M) denotes the channel matrix from the base station m to user mj. We assume the realizations of the channels (H_(mj,m))_(mε(l,1),)

_(ε)

_(are mutually independent and each entries of H) _(mj,m) has independent and identical continuous distribution. The transmit signal from the base station m can be determined as shown in Equation (2).

s _(m)=Σ_(l-1) ^(K) V _(mi) x _(mi)  (2)

Further, transmit power is constrained tr(K[s_(m)s_(m)*])≦P, which can be written as

${{tr}\left( {K\left\lbrack {s_{m}s_{m}^{*}} \right\rbrack} \right)} = {{{tr}\left( {\sum\limits_{i = 1}^{K}\; {v_{m\; i}{E\left\lbrack {x_{m\; i}x_{m\; i}^{*}} \right\rbrack}v_{m\; i}^{*}}} \right)} \leq {P.}}$

We assume equal power transmission for each user and to meet the power constraint we

${E\left\lbrack {x_{m\; i}x_{m\; i}^{*}} \right\rbrack} = {\frac{P}{\kappa \; \beta}.}$

assume

Now, plugging Equation (2) into Equation (1) returns to Equation (3).

Y _(mj)=Σ_(l-1) ^(K) H _(mj,m) V _(mi) x _(mi)+Σ_(l-1) ^(K) H _(mj, m) V _(mi) x _(mi) +z _(mi.)  (3)

To enable the user scheduling at the base station, each user measures channel quality information based on its own channel H_(mh,m), out-of-cell interference channel H_(mj, m) , and random beams (V_(mi))_(ieK) where the transmitter and receiver share the same random beam generation method.

FIG. 2 Illustrates system model when the best K users Π=(π₁, . . . π_(K)) are scheduled in each of cells.

Referring to FIG. 2, for Instance, the scheduled user index is given by (1¹, . . . , 1^(k)) and (2¹, . . . , 2^(k)).

2. Exhaustive User Searching

User mj calculates Channel Quality Information (CQI) for all the possible combinations of transmit beamforming vectors (V_(mi))_(ieK). Prior to define CQIs for exhaustive user scheduling, first several quantities are defined below.

${SIG}_{{mj},i} = {{E\left\lbrack {\left( {H_{{mj},m}V_{m\; i}x_{m\; i}} \right)\left( {H_{{mj},m}V_{m\; i}x_{m\; i}} \right)^{*}} \right\rbrack} = {\frac{P}{K\; \beta}H_{{mj},m}V_{m\; i}V_{m\; i}^{*}H_{{m\; i},m}^{*}}}$ $\text{?} = {{E\left\lbrack {\left( {\sum\limits_{l \neq i}^{K}\; {H_{{mj},m}V_{m\; i}x_{m\; l}}} \right)\left( {\sum\limits_{l \neq i}^{K}\; {H_{{mj},m}V_{m\; i}x_{m\; i}}} \right)^{*}} \right\rbrack} = {\frac{P}{K\; \beta}H_{{mj},m}{\sum\limits_{l \neq k}^{K}\; {V_{m\; l}V_{m\; l}^{*}H_{{mj},m}^{*}}}}}$ $\text{?} = {{E\left\lbrack {\left( {\sum\limits_{i = 1}^{K}\; {H_{{mj},\overset{\_}{m}}V_{m\; l}x_{m\; i}}} \right)\left( {\sum\limits_{i = 1}^{K}\; {H_{{mj},\overset{\_}{m}}V_{m\; l}x_{m\; l}}} \right)^{*}} \right\rbrack} = {\frac{P}{K\; \beta}H_{{mj},\overset{\_}{m}}\text{?}\; V_{m\; l}V_{\overset{\_}{m}l}^{*}H_{{mj},\overset{\_}{m}}^{*}}}$ ?indicates text missing or illegible when filed

SIG_(mj,i) denotes signal covariance matrix at user mj (when V_(mi) is used for the transmission), INT_(IU,mj,K\1) denotes the inter user interference covariance matrix at user mj (when V_(mi) is used for the transmission), and INT_(IU,mj,i) indicates out-of-cell interference covariance matrix at user mj. Then, the achievable rate at user mj after post processing with a post processing matrix w_(mj,i)εC^(N×β) (with W_(mj,i)*W_(mj,i)=I_(β)) can be determined as shown in Equation (4).

$\begin{matrix} {{R_{{mj},i} = {E\mspace{11mu} {\log_{2}\left( \frac{{I_{\beta}{{{w_{{m\; j},i}^{*}\left( {\left. {SIG}_{{mj},i} \right|\text{?}} \right)}w_{{mj},i}}}}}{{I_{\beta} + {{w_{{mj},i}^{*}\left( \text{?} \right)}w_{{mj},i}}}} \right)}}}{\text{?}\text{indicates text missing or illegible when filed}}} & (4) \end{matrix}$

In Equation (4), the post processing matrix W_(mj,i) can be determined by minimizing the interference terms INT_(IU,jm,i)+INT_(IC,mj,i) or by maximizing the achievable rate. We first elaborate how we can conveniently choose CQI when W_(mj,i) is designed to mitigate INT_(IU,mj,i)+INT_(IC,mj,i). Then, we address the case when W_(mj,i) is determined to maximize R_(mj,i).

2.1 Minimum Interference (Inter-User Interference+Out-of-Cell Interference) User Scheduling

In this case, the post processing matrix is determined to mitigate INT_(IU,mj,i)+INT_(IC,mj,i). Without loss of generality, we define the rate achievable with infinite number of users scheduling (J=∞) as R_(mj,i) ^(∞)=E log₂(|I_(β)+{tilde over (W)}_(mj,i)*SIG_(mπ) _(k) {tilde over (W)}_(mj,i)|).

$\begin{matrix} {\begin{matrix} {{R_{{m\; i},i}^{\infty} - R_{{mj},i}} = {{E\; {\log_{2}\left( {{I_{\beta} + {{\overset{\sim}{W}}_{{mj},i}^{*}{SIG}_{m\; \pi_{k}}{\overset{\sim}{W}}_{{mj},i}}}} \right)}} -}} \\ {{E\; {\log_{2}\left( \frac{{I_{\beta} + {{W_{{mj},i}^{*}\left( {{SIG}_{{mj},i} + \text{?}} \right)}W_{{mj},i}}}}{{I_{\beta} + {{W_{{mj},i}^{*}\left( {{INT}_{{IU},{mj},{K\backslash i}} + {INT}_{{IC},{mj}}} \right)}W_{{mj},i}}}} \right)}}} \\ {\leq {E\; {\log_{2}\left( {{I_{\beta} + {{W_{{mj},i}^{*}\left( \text{?} \right)}W_{{mj},i}}}} \right)}}} \\ {= {E\mspace{11mu} {{tr}\left( {\log_{2}\left( {I_{\beta} + {{W_{{mj},i}^{*}\left( \text{?} \right)}W_{{mj},i}}} \right)} \right)}}} \\ {\leq {E\mspace{11mu} {{{tr}\left( {{W_{{mj},i}^{*}\left( \text{?} \right)}W_{{mj},i}} \right)}.}}} \end{matrix}{\text{?}\text{indicates text missing or illegible when filed}}} & (5) \end{matrix}$

From Equation (5), the CQI at user mj when V_(mi) is used for the transmission can be determined as shown in Equation (6).

α_(mj,i)=min _(W) _(mj,i) tr( W _(mj,i)*(INT _(IU,mk,K\i) +INT _(IC,mj))Ŵ _(mj,i))  (6)

Let the eigenvalue decomposition INT_(IU,mj,K\i)+INT_(IC,mj)=U_(mj)Σ_(mj)U_(mi)*. Then, the optimal filter weight W_(mj,i) that minimizes tr( W _(mj,i)*(INT_(IU,mj,K\i)+INT_(IC,mj))Ŵ_(mj,i)) is determined by W_(mj,i)=[u_(mj,N-β+1) . . . u_(mj,N)] where u_(mj,i) denotes the ith column of U_(mj).

Given CQI defined in Equation (6), user mj feeds back (α_(mj,i))_(ieK), i.e., user mj evaluates interference (inter-user interference+out-of-cell interference) powers corresponding to each of K beam forming vectors. Then, receiver m determines the best K user set Π=(π₁, . . . , π_(K))⊂I to minimize the sum interference as shown in Equation (7).

Π=(π₁, . . . ,π_(K))=argmin_(Π)=({tilde over (π)} ₂ _(, . . . ,{tilde over (π)}) _(K) _(),) JΣ _(i-1) ^(K)α_(m)

_(i)  (7)

In Equation (4), {tilde over (Π)}=({tilde over (π)}₁, . . . , {tilde over (π)}_(K)) with {tilde over (π)}₁≠{tilde over (π)}₂≠ . . . ≠{tilde over (π)}_(K) denotes the all the possible K user permutation in J. Hence, finding the optimal user set Π=(π₁, . . . , π_(K)) requires total

${\begin{pmatrix} I \\ K \end{pmatrix}{KI}} = \frac{J!}{\left( {J - K} \right)!}$

times of computations of Σ_(i-1) ^(K)α_(ui)

_(i) .

2.2 Maximum Sumrate User Scheduling

In this case, the post processing matrix is determined to maximize R_(mj,i) in Equation (4). For simplicity, denote INT_(mj,i)=INT_(IU,mj,K\i)+INT_(IC,mj).

Then, R_(mj,i) in Equation (4) is lower bounded by Equation (8).

$\begin{matrix} {R_{{mj},i} = {{E\mspace{11mu} {\log_{2}\left( \frac{\left. I_{\beta} \middle| {{W_{{mj},i}^{*}\left( {SIG}_{{mj},i} \middle| {INT}_{{mj},i} \right)}W_{{mj},i}} \right.}{{I_{\beta} + {{W_{{mj},i}^{*}\left( {INT}_{{mj},i} \right)}W_{{mj},i}}}} \right)}} \geq {E\mspace{11mu} {\log_{2}\left( \frac{{{W_{{m\; i},i}^{*}\left( {SIG}_{{mj},i} \right)}W_{{mj},i}}}{{I_{\beta} + {{W_{{mj},i}^{*}\left( {INT}_{{mj},i} \right)}W_{{mj},i}}}} \right)}{\ldots \mspace{14mu}.}}}} & (8) \end{matrix}$

The optimal W_(mj,i) that maximize the lower bound of Equation (8) is found as follows.

With a congruence transformation, there exists X_(mj)εC^(N×N) such that:

X _(mi) *SIG _(mj,i) X _(mj)=diag(c ₁ , . . . ,c _(N))=C _(d)  (9)

where c₁≧C₂≧ . . . ≧C_(N)≧0, and

X _(mi)*(I _(N) +INT _(mj,i))X _(mj)=diag(s ₁ , . . . ,s _(N))=S _(d)  (10)

where s_(N)≧S_(N-1)≧ . . . ≧s₁>0. Then, from Equation (9) and Equation (10),

X _(mj) *SIG _(mj,i) X _(mj) C _(d) ⁻¹ =X _(mi)*(I _(n) +INT _(mj,i))X _(mj) S _(d) ⁻¹

SIG _(mj,i) X _(mj)=(I _(n) +INT _(mj,i))X _(mj) S _(d) ⁻¹ C _(d)

Implying

$\; {{{{SIG}_{{mj},i}x_{{m\; i},1}} = {{\frac{c_{1}}{s_{1}}\left( {I_{N} + {INT}_{{mj},i}} \right)x_{{mj},1}} = {{\lambda_{l}\left( {I_{N} + {INT}_{{mj},i}} \right)}x_{{mj},1}}}},}$

where x_(mj,l) denotes the lth column of X_(mj) for l=1, 2, . . . , N. Thus, λ_(l)c₁/s₁ is the generalized eigenvalue of SIG_(mj,i) and (I_(N)+INT_(mj,i))) such that λ₁≧λ₂≧ . . . ≧λ_(N).

Hence,

$\frac{{{W_{{mj},i}^{*}\left( {SIG}_{{mj},i} \right)}W_{{mj},i}}}{{I_{\beta} + {{W_{{mj},i}^{*}\left( {INT}_{{mj},i} \right)}W_{{mj},i}}}} \leq {\prod\limits_{i = 1}^{\beta}\; {\lambda_{i}.}}$

The equality is achieved by choosing the first β generalized eigenvectors associated with the generalized eigenvalues λ₁, λ₂, . . . λ_(β), as shown in Equation (11).

W _(mj,i) =[x _(mj,1) x _(mj,2) . . . x _(mj,β)]  (11)

Now, given W_(mj,i) in Equation (11), CQI is defined as

$\alpha_{{mj},i} = {{\log_{2}\left( \frac{{{W_{{mj},i}^{*}\left( {SIG}_{{mj},i} \right)}W_{{mj},i}}}{\left. I_{\beta} \middle| {{W_{{mj},i}^{*}\left( {INT}_{{mj},i} \right)}W_{{mj},i}} \right.} \right)}.}$

User mj feeds back (α_(mj,i))_(ieK) to receiver m and the receiver m determines the best K user's index set Π=(π₁, . . . , π_(K)) to maximize the sum rate such that

${\Pi = {\left( {\pi_{1},\ldots \mspace{14mu},\pi_{K}} \right) = {\arg \; {\max_{\Pi = {({{\overset{\sim}{\pi}}_{1},\mspace{11mu} \ldots \mspace{14mu},{\overset{\sim}{\pi}}_{K}})}}{J{\sum\limits_{i = 1}^{K}\; \text{?}}}}}}},{\text{?}\text{indicates text missing or illegible when filed}}$

where {tilde over (π)}₁≠{tilde over (π)}₂≠ . . . ≠{tilde over (π)}_(K).

3. Opportunistic Interference Alignment

The exhaustive use scheduling in Section 3 requires

$\frac{J!}{\left( {\text{?} - K} \right)\text{?}}$ ?indicates text missing or illegible when filed

times of calculation for finding the best user ordering. In this section, we develop a low complex user scheduling scheme which is based on ordering users in the interference alignment planes (P_(m))_(m├(1,2)) where P_(m)εC^(M×N) and P_(m)*P_(m)=I_(N). Now we rewrite Equation (3) as Equation (12).

Y _(mj) =H _(mj,m) P _(m) s _(m) +H _(mj,m) P _(m) s _(m) +z _(mi)  (12)

In Equation (12),

$s_{m} = {\sum\limits_{i = 1}^{x}\; {V_{m\; i}{x_{m\; i}.}}}$

The V_(mi) satisfies V_(mi)εC^(N×β) and tr(V_(mi)*V_(mi))=β. We have the same equality power constraints t=(K[s_(m)s_(m)*])=P and E[x_(mi)x_(mi)*]=P/Kβ, as in Section 3. In this approach, contrary to Section 3, the interference alignment plains (P_(m))_(m├(1,2)) are not used for data transmission, but used for scheduling users. Once the users have been opportunistically scheduled, the transmitter performs zero-forcing by designing (V_(mi))_(ieK).

The CQI consists of one α_(mj) and one F_(mj)εC^(β×N). Analogous to Section 3, for scheduling the users, the post processing matrix W_(mj)εC^(N×β) (within W_(mj)*W_(mj,)=I_(β)) can be determined by minimize the out-of-cell interference or to maximize the achievable rate.

Define:

SiG _(mj) =E[(H _(mj,m) P _(m) s _(m))(H _(mj,m) P _(m) s _(m))*]=PH _(mj,m) P _(m) P _(m) *H _(mj,m)*

and

INT _(ICm,j) =E[(H _(mj, m) P _(m) s _(m))(H _(mj, m) P _(m) s _(m))*]=PH _(mj,m) P _(m) P _(m) *H _(mj, m) *,

where SIG_(mj) denotes the signal covariance matrix at user mj and INT_(IC,mj) denotes the out-of-cell interference covariance matrix at user mj. Then, the mutual information after post processing between s_(m) and W_(mi)*Y_(mi) can be expressed as shown in Equation (13)

$\begin{matrix} {I_{mj} = {{\log_{2}\begin{pmatrix} {{I_{\beta} + {{w_{mj}^{*}\left( {{SIG}_{mj} + {INT}_{{IC},{mj}}} \right)}w_{mj}}}} \\ {{I_{\beta} + {{w_{mj}^{*}\left( {INT}_{{IC},{mj}} \right)}w_{m\; i}}}} \end{pmatrix}}\mspace{11mu} {\ldots \mspace{14mu}.}}} & (13) \end{matrix}$

3.1 Minimum Out-of-Cell Interference User Scheduling

Define CQI at user mj as shown in Equation (14).

α_(mj)=min _(W) _(mj) tr( W _(mj)*(INT _(IC,mj))Ŵ _(mj))  (14)

Let the eigenvalue decomposition INT_(IC,mj)=U_(mj)Σ_(mj)U_(mj)*. Then, the optimal filter weights W_(mj) minimizes tr( W _(mj)*(INT_(IC,mj))Ŵ_(mj)) is determined by W_(mj)=[n_(mj,N-β+1) . . . n_(mj,N)] where u_(mj,i) denotes the ith column of U_(mj).

Given CQI defined in Equation (4), user mj feeds back one α_(mj) and a matrix F_(mj)εC^(β×N). The F_(mj) is defined as the direction of the post processed channel matrix W_(mj)*H_(mj,m)P_(m) where the direction of W_(mj)*H_(mj,m)P_(m) can be extracted by SVD W_(mj)*H_(mj,m)P_(m)=U_(mj)R_(mi)V_(mi)* as F_(mj)=[v_(mj,1) . . . v_(mj,β)]*. Note that we can employ matrix codebook or elementwise quantization for delivering F_(mj) to the base station m.

Then, receiver m determines K user set Π=(π₁, . . . , π_(K)) to minimize the sum out-of-cell interference such that

${\Pi = {\left( {\pi_{1},\ldots \mspace{14mu},\pi_{K}} \right) = {\arg \; \min_{\Pi = {({{\overset{\sim}{\pi}}_{1},\mspace{11mu} \ldots \mspace{14mu},{\overset{\sim}{\pi}}_{K}})}}}}},{J{\sum\limits_{i = 1}^{K}\; \text{?}}},{\text{?}\text{indicates text missing or illegible when filed}}$

where {tilde over (Π)}=({tilde over (π)}₁, . . . , {tilde over (π)}_(K)) with {tilde over (π)}₁≠{tilde over (π)}₂≠ . . . ≠{tilde over (π)}_(K) denotes the all possible K user combinations in J. Hence, finding the optimal user set Π=(π₁, . . . , π_(K)) requires total

$\begin{pmatrix} J \\ K \end{pmatrix} = \frac{J!}{{K!}{\left( {J - K} \right)!}}$

times of computation for Σ_(l-1) ^(K)α_(m)

_(i) _(k). The complexity for finding Π=(π₁, . . . , π_(K)) can be decreased if we use an efficient sorting algorithms for sorting all (α_(mj))_(ieJ) such that (α_(mπ) ₁ , . . . , α_(mπ) _(K) )≦(α_(mj))_(i├̂π).

This easily accomplished by using fast sorting algorithms which usually requires on average I log₂(J) comparisons. If we consider one addition is equivalent to one comparison, for choosing the user the exhaustive search in Section 3 requires total

$\frac{J!}{\left( {\text{?} - K} \right)!}K$ ?indicates text missing or illegible when filed

times of additions while the opportunistic interference alignment requires only I log₂(J) times of comparisons.

After selecting the users Π=(π₁, . . . , π_(K)) the transmitter determines the transmit filter weight as zero-forcing weight. Consider a stacked matrix

$F_{m\; \Pi} = \begin{bmatrix} F_{m\; \pi_{1}} \\ F_{m\; \pi_{2}} \\ \vdots \\ F_{m\; \pi_{K}} \end{bmatrix}$

and the inverse of F_(mΠ) as F_(mΠ) ⁻¹={tilde over (V)}_(mΠ). To meet the power constraint each column of {tilde over (V)}_(mΠ) is normalized as one and we denote the normalized one as V_(mΠ). Then, the zero-forcing transmit vectors (V_(mπ) _(i) )_(ieΠ) for users (mπ_(i))_(ieΠ) are mapped by V_(mΠ)=[V_(mπ) ₁ , V_(mπ) ₂ , . . . , V_(mπ) _(K) ].

3.2 Maximizing Sumrate User Scheduling

The I_(mj) in Equation (13) is lower bounded as shown in Equation (15).

$\begin{matrix} {I_{mj} = {{\log_{2}\begin{pmatrix} {{I_{\beta} + {{w_{mj}^{*}\left( {{SIG}_{mj} + {INT}_{{IC},{mj}}} \right)}w_{mj}}}} \\ {{I_{\beta} + {{w_{mj}^{*}\left( {INT}_{{IC},{mj}} \right)}w_{mj}}}} \end{pmatrix}} \geq {E\mspace{11mu} {\log_{2}\left( \frac{{{W_{mj}^{*}\left( {SIG}_{mj} \right)}W_{mj}}}{{I_{\beta} + {{W_{mj}^{*}\left( {INT}_{{IC},{m\; i}} \right)}W_{m\; i}}}} \right)}{\ldots \mspace{14mu}.}}}} & (15) \end{matrix}$

The optimal W_(mj) that maximize the lower bound of Equation (15) is the first β generalized eigenvectors of (SIG_(mj*)I_(N)+INT_(IC,mj)). Analogous with Section 3.2, we define the generalized eigenvalue matrix of (SIG_(mj*)I_(N)+INT_(IC,mj)) as X_(mj) where the ith columns of X_(mj) corresponds to ith dominant eigenvalue λ_(i). Hence, the bound of Equation (15) is maximized by selecting W_(mj) as below shown in Equation (16).

W _(mj) =[x _(mj,1) x _(mj,2) . . . x _(mj,β)]  (16)

Now, given W_(mj) in Equation (11), we define the CQI as

$\alpha_{mj} = {{\log_{2}\left( \frac{{{W_{mj}^{*}\left( {SIG}_{mj} \right)}W_{mj}}}{\left. I_{\beta} \middle| {{W_{mj}^{*}\left( {INT}_{{IC},{m\; i}} \right)}W_{m\; i}} \right.} \right)}.}$

User mj feeds back one α_(mj) and a matrix F_(mj)εC^(β×N) to receiver m and the receiver m determines the best K user's index set Π=(π₁, . . . , π_(J)) to maximize the sum rate such that

${\Pi = {\left( {\pi_{1},\ldots \mspace{14mu},\pi_{K}} \right) = {\arg \; \max_{\Pi = {({{\overset{\sim}{\pi}}_{1},\ldots \mspace{14mu},{\overset{\sim}{\pi}}_{K}})}}}}},{J{\sum\limits_{i = 1}^{K}\; \text{?}}},{\text{?}\text{indicates text missing or illegible when filed}}$

where {tilde over (π)}₁≠{tilde over (π)}₂≠ . . . ≠{tilde over (π)}_(K).

After selecting the users Π=(π₁, . . . , π_(K)) the transmitter determines the transmit filter weight as zero-forcing weight. Consider a stacked matrix:

${F_{m\; \Pi} = \begin{bmatrix} F_{m\; \pi_{1}} \\ F_{m\; \pi_{2}} \\ \vdots \\ F_{m\; \pi_{K}} \end{bmatrix}},$

and the inverse of F_(mΠ) as F_(mΠ) ⁻¹={tilde over (V)}_(mΠ). To meet the power constraint each column of {tilde over (V)}_(mΠ) is normalized as one and we denote the normalized one as V_(mΠ). Then, the zero-forcing transmit vectors (V_(mπ) _(i) )_(ieΠ) for users (mπ_(i))_(ie5Π) are mapped by V_(mΠ)=[V_(mπ) ₁ V_(mπ) ₂ . . . V_(mπ) _(K) ].

In this section, we evaluate the proposed user scheduling schemes where the transmitter serves the best K=2 users among J users. The numbers of the transmit antennas and receive antennas are given by M=Kβ+β and N=Kβ. Throughout the simulation we assume single date stream transmission per user, we use MIUS to denote the minimum interference user scheduling (MIUS) presented in Section 2.1 and 3.1, and MSUS is used to denote the maximum sumrate user scheduling (MSUS) in Section 2.2 and 3.2.

FIG. 3 Illustrates sumrates per cell for MIUS schemes.

Referring to FIG. 3, ‘Exhaustive’ implies exhaustive user scheduling scheduling scheme in Section 2 and ‘OIA’ denotes the opportunistic interference alignment scheme in Section 3. Exhaustive user searching only needs to feedback K real CQI values (α_(mj,i))_(ieK) and OIA requires to send back one real CQI α_(mi) and the direction of the post processed channel matrix F_(mj)εC^(1×K) which is composed of 2K real values. The exhaustive user scheduler at the receiver need to compute

$\frac{J!}{\left( {\text{?} - K} \right)!}K$ ?indicates text missing or illegible when filed

folds of computation, while the OIA only needs to compute I log₂(J) folds of operations. As can be seen from FIG. 3, OIA shows better performance. As SNR increases the throughput enhancement for OIA is significant compared to exhaustive user selection.

FIG. 4 Illustrates sumrate performance for MSUS schemes.

Referring to FIG. 4, Compared to MIUS, MSUS yields higher throughput gain because the post processing matrix is chosen to maximize the individual throughput. The similar trend is observed as FIG. 3. However, compared to FIG. 3, the optimality of the post processing matrix design in MSUS promises the significant throughput gain especially for OIA.

In FIG. 3 and FIG. 4, we consider the feedback scenario where each user in OIA feeds back 2K+1 real scalars. In this simulation study, we investigate the effect of the quantization error when we employ the matrix codebook to quantize the post processed channel F_(mj)εC^(1×K).

FIG. 5 illustrates the sumrate performance for MSUS when we employ 6 bits and 8 bits of matrix codebook. For the sake of the simplicity the codebook is randomly generated.

As can be seen from the FIG. 5, OIA can achieve better performance than exhaustive user searching at high SNR with reduced feedback amount.

While the present invention has been particularly shown and described with reference to certain embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the following claims and their equivalents. 

What is claimed is:
 1. A method for opportunistic user scheduling of two-cell multiple user Multiple Input Multiple Output (MIMO) by a base station, the method comprising: broadcasting signals through random beams to users; and receiving Channel Quality Information (CQI) from a best K user set, wherein the CQI is calculated based on all possible combinations of transmit beamforming vectors.
 2. The method of claim 1, wherein the best K user set is determined to minimize sum interference.
 3. The method of claim 1, wherein the best K user set is determined to maximize sum rate.
 4. The method of claim 1, wherein the best K user set is determined to minimize sum out-of-cell interference.
 5. The method of claim 1, wherein the best K user set is determined to minimize sum out-of-cell interference. 